Search Results (1,158)

Proton-exchange membrane fuel cell degradation is one of the factors that limit the deployment of this technology in the automotive market. Accelerated Stress Tests (ASTs) are the standard tool for assessing durability during design and development phases, thanks to their shorter duration, but there are concerns about their representativeness of degradation phenomena appearing in real driving conditions. The present study couples a semi-empirical multi-layer degradation model with a validated reduced-order physical model of the cell to evaluate degradation occurring in both kinds of conditions. First, the phenomenological model is used to predict the decay in the polarization curve when running continuous real driving cycle and accelerated stress profiles up to a total of 1000 h. Then, the physical reduced-order model is calibrated to the produced polarization curves at each cumulative time by introducing three degradation factors: two associated with the electrochemical surface area of the anode and cathode catalyst layers, and another one related to the membrane conductivity. Additionally, the results of the physical model are evaluated to identify the occurrence of local conditions that can induce degradation. The results show that a properly designed accelerated stress test provides approximately 10 times faster degradation while maintaining similar degradation factors and physical representativeness. Full article

The oxygen reduction reaction (ORR) remains a major bottleneck in terms of kinetics and durability in fuel cells and zinc–air batteries (ZABs). Metal–organic frameworks (MOFs) are versatile precursors for platinum-group metal (PGM)-free ORR electrocatalysts because their metal distribution, ligand chemistry, guest confinement, morphology, and porosity can be controlled before pyrolysis. This review examines how these precursor characteristics and subsequent thermal conversion govern metal migration; heteroatom retention; carbon ordering; pore evolution; and, ultimately, the nuclearity, coordination environment, and accessibility of the resulting active sites. Recent advances in conventional and asymmetric M–N_x single-atom sites, dual- and multi-atom sites, and single-atom–cluster or nanophase interfaces are critically evaluated, with particular attention to the evidence supporting structural assignments, activity, selectivity, and durability. Half-cell performance is further related to practical fuel-cell and ZAB operation by considering catalyst loading, ionomer or electrolyte contact, gas and water transport, and catalyst-layer degradation. Further progress will require simultaneous optimization of active-site structure, accessible-site density, hierarchical porosity, carbon stability, and electrode architecture, together with standardized testing protocols for reliable translation from rotating disk electrode measurements to working cathodes. Full article

Electric–hydrogen hybrid microgrids integrating distributed renewables with electrolysers can efficiently convert dispersed renewables into hydrogen, meeting local electricity demands. However, intermittent and uncertain renewables together with sudden load changes can cause severe bus voltage fluctuations and degrade power quality, especially in off-grid microgrids. Furthermore, the electrolyser’s auxiliary units require stable AC power supply even during fault events, which most likely occur at AC–DC converters. To ensure stability during renewable/load fluctuations and converter faults, this paper proposes a fault-resilient coordinated voltage control scheme by combining PI with piecewise active disturbance rejection control to mitigate DC bus voltage fluctuations during transient disturbances and regulates AC-side fuel cells via control switching to stabilise AC voltage after the complete disconnection converter fault. The scheme is tested using a simulated kW-scale electric–hydrogen hybrid microgrid in various operating scenarios and compared with conventional methods that combine PI with PI or linear active disturbance rejection control. The simulation results show that the proposed control scheme improves DC bus voltage stability by 14% during transient renewable/load fluctuations via the incorporation of PADRC and enhance system resilience against AC–DC converter faults through the synergy of batteries and fuel cells, which construct the voltage (and frequency) of DC and AC buses, respectively. Full article

High-frequency operational records from new energy vehicles (NEVs) are increasingly used to support data-driven sustainable mobility applications, including condition monitoring, energy management, and battery-state estimation. In practice, these records are often incomplete because of sensor faults, communication dropouts, and rapidly changing operating environments, which can distort downstream analyses and reduce the reliability of vehicle-state assessment. This study proposes MDCformer, a multi-period nonstationary modeling framework for reconstructing missing vehicle-speed measurements from multivariate NEV operational time series. MDCformer integrates timestamp-derived temporal descriptors, convolution-enhanced self-attention, and de-stationary attention modulation. The temporal descriptors provide explicit calendar context, the convolutional attention module strengthens local signal consistency before global dependency modeling, and the de-stationary module reintroduces time-varying statistical cues that may be suppressed by normalization. Because the battery electric vehicle (BEV) and fuel cell vehicle (FCV) datasets used in this study cover approximately 18 days and 2.6 days, respectively, the empirical evidence mainly supports daily and short-horizon periodic cues; longer-cycle descriptors are retained as extensible components for longer fleet-level records. Experiments on two real-world NEV datasets show that MDCformer consistently outperforms representative deep-learning baselines under missing rates from 10% to 50%. At a 10% missing rate, compared with the vanilla Transformer baseline, MDCformer reduces root mean square error (RMSE) and mean absolute error (MAE) by 11.35% and 19.00% on the BEV dataset and by 41.41% and 54.83% on the FCV dataset, respectively. Additional scenario-specific tests and downstream state-of-charge prediction further indicate that the reconstructed data preserve more useful temporal structure for sustainable intelligent-transportation analytics. These findings demonstrate the potential of reliable data reconstruction for improving the robustness of intelligent vehicle monitoring and supporting data-driven sustainable transportation applications. Full article

This study investigates real-time model predictive energy management for portable air-cooled fuel cell/lithium-ion battery hybrid power systems. To capture the coupled electrical and thermal behavior of the system while maintaining computational efficiency for online control, a control-oriented lumped-parameter model is developed. The model describes the fuel cell voltage characteristics and thermal dynamics, the lithium-ion battery state of charge (SOC), and the level of hydrogen (LOH) in the hydrogen tank. Based on this model, a model predictive control (MPC)-based energy management strategy is proposed to coordinate the power distribution between the fuel cell and lithium-ion battery subject to power, ramp-rate, SOC, and LOH constraints. The proposed strategy is compared with a conventional rule-based strategy under rated-power, short-duration load variation, and long-duration load variation conditions. Simulation results show that the proposed strategy smooths fuel cell power, maintains the battery SOC within a reasonable range, and improves coordinated energy utilization. Hardware-in-the-loop experiments using STM32 controllers further verify its real-time feasibility. Compared with the rule-based strategy, the proposed method reduces the maximum fuel cell power variation rate by 85.7% in the HIL test, demonstrating improved fuel cell power stability. Full article

To encourage the use of hydrogen energy and improve the dependability of hydrogen fuel cells, this study develops a two-stage semi-supervised framework for anomaly pattern discovery and codification. Isolation Forest, One-Class Support Vector Machine, and Local Outlier Factor are first applied to unlabeled time series data; six supervised classifiers are subsequently trained on ensemble-derived pseudo-labels to learn these patterns efficiently. The analysis uses a reproducible 20% sample (random seed 42) of 185,721 observations from four fuel cells in the NASA Prognostics Data Repository, yielding 37,144 observations. The three unsupervised algorithms exhibit distinct detection patterns, with 280 common detections (15.07% of each model’s flagged observations and 0.754% of the sample). A consensus-weighted ensemble identifies 1296 potential anomalies (3.49%). Random Forest best reproduces the ensemble pseudo-labels, with 99.365% testing accuracy; this value measures pattern learnability and is not independent verification of physical faults. Gini importance identifies power difference, capacity, load power, load current, measured power, and load resistance as the principal predictive variables. Because independently confirmed fault labels and maintenance outcomes are unavailable, the reported detections are interpreted as systematic deviations requiring electrochemical or expert verification. Full article

The coupled effects of power distribution and stack temperature strongly influence hydrogen economy, durability, and operating stability in proton exchange membrane fuel cell (PEMFC) vehicles. This study proposes an integrated energy–thermal management strategy based on the multi-agent deep deterministic policy gradient (MADDPG) algorithm for a PEMFC hybrid bus. The energy management agent regulates PEMFC power using vehicle demand, battery state of charge, and stack temperature, while the thermal management agent controls coolant and air mass flow rates using temperature errors and commanded PEMFC power. Under the unseen CHTC-C cycle, MADDPG reduces equivalent hydrogen consumption by 0.49% and 1.79% compared with SAC and DDPG, respectively, while remaining 2.92% above the offline dynamic programming benchmark. Under an independent real-world bus cycle, MADDPG reduces the maximum stack outlet temperature deviation by 98.56% and 98.07%relative to SAC and MPC, respectively. Additional tests under ambient temperature and aging variations show bounded thermal responses without retraining, and HIL experiments confirm real-time execution at a 1 s control period. Overall, the proposed strategy improves energy economy, thermal regulation, adaptability, and real-time applicability through coordinated power–temperature information exchange. Full article

Fuel cell electric vehicles (FCEVs) require energy management strategies that can balance hydrogen economy, battery utilization, component protection, and real-time control under varying driving conditions. This paper proposes an Adaptive-Modality Deep Deterministic Policy Gradient and Model Predictive Control hierarchical energy management strategy (AMDDPG–MPC HEMS). In the proposed architecture, the upper-level AMDDPG controller identifies driving-condition patterns and generates adaptive weights for hydrogen consumption, battery power, and state-of-charge regulation, while the lower-level MPC controller performs constrained power allocation between the fuel cell and battery. To improve adaptability, the AMDDPG algorithm incorporates an adaptive modality perception mechanism that extracts driving-condition features and a multi-scale reward mechanism that coordinates short-term energy-saving objectives with long-term component-protection requirements. A dedicated weight-scheduling and switching mechanism is also introduced to ensure smooth transitions between operating conditions. The proposed strategy is evaluated under the World Light Vehicle Test Cycle and Urban Dynamometer Driving Schedule and compared with rule-based, equivalent consumption minimization, and fixed-weight MPC strategies. The results show that the AMDDPG–MPC HEMS achieves the lowest equivalent hydrogen consumption, with reductions of 18.853% and 11.732% relative to the rule-based strategy under the two driving cycles, respectively. It also improves fuel-cell operating efficiency and maintains feasible battery SOC regulation. These results demonstrate the effectiveness and engineering potential of the proposed hierarchical energy management strategy. Full article

Reversible Solid Oxide Cells (rSOCs) are highly efficient energy conversion systems for power generation in fuel cell mode (SOFC) and energy storage in electrolysis mode (SOEC). These devices are typically manufactured through multi-step processing routes based on conventional functional ceramic fabrication techniques, such as tape-casting, extrusion, screen-printing and spraying. In this work, an alternative mold-casting approach is proposed for the fabrication of planar electrolyte-supported rSOCs. Electrolytes made of 8 mol% yttria-stabilized zirconia (YSZ) were prepared via an aqueous gel-casting process using agarose as the gelling agent. The casting molds were fabricated by 3D printing with polylactic acid (PLA) filament. Dense electrolytes with well-controlled geometries were successfully obtained. Complete cells were produced using porous Ni–YSZ as a fuel electrode and porous lanthanum strontium manganite–YSZ. The cells were microstructurally characterized, and their electrochemical performance was evaluated under both SOFC and SOEC operating conditions at 800–900 °C. At 900 °C, the cell achieved a peak power density of 220 mW cm⁻² in fuel cell mode and an injected current density of 340 mA cm⁻² at 1.3 V in electrolysis mode. Mid-term galvanostatic testing in SOFC mode at 850 °C for 400 h demonstrated good durability and structural stability of the fabricated cells. After the initial stabilization period, the cell exhibited a low degradation rate of 3 mV kh⁻¹. Full article

Under ISO 50001, energy performance is monitored through Energy Performance Indicators (EnPIs) and energy baselines. In practice, the energy-to-production ratio (kWh/t) is commonly adopted by default and validated solely by the coefficient of determination ($R^2$), which is insensitive to systematic bias, to the base load contained in the intercept, and to the residual structure that reveals an omitted explanatory variable. This work organizes well-established statistical and engineering checks into a sequential, four-outcome decision procedure anchored to the diagnosis of Significant Energy Uses (SEUs): retain the simple ratio, adopt a regression baseline with the same variable, switch to the SEU-associated variable, or reject the model as structurally misspecified. Relative to common practice, the procedure makes three methodological corrections explicit: in-sample NMBE is identically zero for OLS models with an intercept and is therefore defined out of sample; residual diagnostics are evaluated against exact, design-specific Durbin–Watson critical values with a Šidák-corrected family-wise error of 0.044–0.050 (versus ≈0.14 uncorrected); and the candidate-variable step uses a partial F-test on nested models, since the naive residual-versus-variable regression is attenuated by collinearity with production. The procedure is characterized on synthetic data with known truth ($N=1000$ replicates per cell): against an interannual drift of ≈2%/yr, its sensitivity reaches 1.00 at $n=72$ months while an $R^2$-only criterion has sensitivity 0.00, and with a base-load fraction of ≈0.28 the $R^2$-only rule retains the biased ratio in 100% of the replicates; specificity under a correct ratio is 0.95–0.96, and the adopted thresholds lie in a stable region of the ($R^2$, $f_0$) sensitivity sweep. The procedure is then demonstrated on six industrial cases; most notably, in a fuel oil power plant a pooled baseline with $R^2=0.998$ is rejected (Durbin–Watson $=0.79$ versus an exact critical value of 1.64; runs test $p<0.001$) because of drift in specific fuel consumption that $R^2$ cannot detect, and its out-of-sample validation over 37 rolling origins shows that an aggregated bias of $+0.31\%$ can mask an origin-to-origin drift from $-1.7\%$ to $+2.1\%$. The contribution is not a new indicator or a new statistic, but the integration of indicator selection and multistage statistical validation into a single auditable decision procedure whose operating characteristics are quantified. Full article

The use of synthetic fuels produced from CO₂ requires efficient production technologies and evaluation of their application in energy systems. This study combines experimental tests of a dual-fuel engine powered by n-propanol and diesel fuel with modeling of electrochemical n-propanol synthesis from CO₂. The aim was to determine the optimal n-propanol share and investigate the performance of a tandem electrochemical reactor. Increasing the energy share of n-propanol reduced CO and CO₂ emissions. The highest engine efficiency of 33.94% was achieved at a 50% energy share of n-propanol, representing an increase of 1.5% compared with diesel-only operation. At this operating point, CO and CO₂ emissions were reduced by 87.7% and 18.5%, respectively, compared with diesel-only operations. This point was selected for further analysis. A mathematical model of a tandem electrochemical reactor was developed. The system included CO₂-to-CO conversion followed by n-propanol synthesis. The energy efficiencies of the CO generation and n-propanol synthesis reactors were 47.77% and 23.07%, respectively. Faradaic efficiency and cell voltage were the main factors affecting reactor performance. The overall tandem reactor efficiency ranged from 12% to 22%. The results confirm the potential of n-propanol as a dual-fuel engine fuel and identify key directions for improving electrochemical CO₂ conversion. Full article

High-speed permanent magnet motors, due to their high speed, compact size, and light weight, are increasingly widely used in renewable energy systems, electric pump drives, fuel cell air compressors, and other fields. As a core component of high-speed permanent magnet motors, the reasonable design of the rotor system structure directly affects motor stability. To ensure the safe and reliable operation of high-speed permanent magnet motors, this paper designs the structure of a certain type of high-speed electric pump rotor. First, the actual interference amount between the rotor permanent magnet and the high-temperature alloy sleeve under high-speed and high-temperature conditions is considered, and radial and tangential stress analyses are performed on both the rotor and high-temperature alloy sleeve to determine the optimal interference amount. Second, based on rotor dynamics and fluid–structure coupling theory, the natural frequency and critical speed of rotors under wet and dry modals are studied; on this basis, harmonic response analysis and fatigue assessment were conducted; furthermore, an elastoplastic mechanical model of the rotor sleeve is introduced to analyze the effects of interference amount and rotational speed on the sleeve’s yield failure; finally, the dynamic safety of the high-speed rotor structure is verified through modal tests and overspeed operation tests. The results show that the optimal interference amount for the rotor is 0.02 mm; the first-order critical speeds in both dry and wet modals are well above the rated speed of 40,000 rpm, with no risk of resonance; the minimum cycle for fatigue life is 6.9 × 10⁵, meeting usage requirements; the equivalent force on the rotor sleeve increases with speed and interference amount; when the speed exceeds 44,000 rpm, the sleeve undergoes plastic deformation and failure; modal test error is less than 5%. This paper provides theoretical basis and experimental support for the rotor structure design and strength evaluation of high-speed permanent magnet motor drive equipment. Full article

Additive manufacturing provides a fabrication route for electrode components with controlled macrostructure; however, printable carbon inks that also incorporate oxygen reduction reaction active precursors remain underdeveloped. Here, XC-72 carbon was combined with selected metal precursors to prepare N–C, Fe–N–C, and Pt-containing carbon ink formulations for direct ink writing. The precursor mixtures were incorporated into a polyurethane-based matrix, pyrolyzed at 900 °C, and characterized using X-ray diffraction, oscillatory rheology, rotating ring-disk electrode measurements, Brunauer–Emmett–Teller surface-area analysis, and scanning electron microscopy. XRD confirmed retention of carbon diffraction features and the formation of metal-containing crystalline phases after pyrolysis. Oscillatory rheology showed storage moduli exceeding loss moduli for the tested formulations, indicating elastic-dominant behavior suitable for shape retention during printing. For the PGM-free formulations, incorporation of nitrogen and iron precursors improved ORR onset potential, half-wave potential, limiting current density, and electron-transfer selectivity relative to the carbon control. BET analysis showed a decrease in accessible surface area after precursor incorporation, consistent with partial pore blocking or structural modification during pyrolysis. These results establish a printable formulation platform for ORR-active carbon-based inks, while future work is required to isolate the effects of printed architecture, pore hierarchy, and durability under fuel-cell operating conditions. Full article

Outdoor Internet of Things (IoT) sensor nodes require battery systems for which their behaviour and implementation limits are characterized under low-temperature and variableload conditions. This study documents a LiFePO₄ battery-pack prototype integrating a BQ29729DSET protection IC and a configured MAX17055ETB+T fuel gauge and descriptively compares its discharge runtime with that of a reference pack with a similar nominal capacity, comprising three parallel Samsung ICR18650-26H cells. Tests were conducted at −30 °C, 8 °C, and 25 °C under nominal load settings of 50, 100, and 200 mA. At 8 °C and 25 °C, the two configurations showed similar runtimes and nominal-current-based calculated capacities. At −30 °C, the LiFePO₄ assembly ran for 89.1 versus 66.0 h at 50 mA and 44.7 versus 37.2 h at 100 mA. An analysis based on the typical MCP1700 dropout characteristic bounds the portions of these LiFePO₄ runtimes recorded below the assumed regulation threshold at approximately 1.1 h and 0.9 h, respectively; even subtracting those complete intervals leaves positive differences of 33.3% and 17.7% relative to the reference runtimes. Complete current logs were unavailable; therefore, capacity and energy are reported only as nominal-current estimates. In the ICR18650-26H reference pack at −30 °C, the calculated capacity increased anomalously from 3302 to 4087 mAh as the nominal setting increased from 50 to 200 mA. The ICR cutoff remained above the estimated regulator-dropout thresholds, so dropout does not explain the anomaly; temperature, conditioning, and run-order effects cannot be excluded. Protection trip points and fuel-gauge accuracy were not experimentally verified. Our contribution is therefore reproducible design documentation combined with preliminary low-temperature runtime evidence rather than validation of a fully monitored and protected battery pack. Full article

CO poisoning remains a major challenge for hydrogen fuel cells, and rotating disk electrode (RDE) measurements are widely used for preliminary screening of CO-tolerant catalysts. However, variations in key testing parameters, including catalyst loading, CO exposure time, and linear sweep voltammetry (LSV) scan rate, can substantially affect the apparent CO tolerance response and compromise cross-study comparability. Here, Pt/C was employed as a model catalyst to systematically decouple the effects of these parameters on CO tolerance evaluation of the hydrogen oxidation reaction (HOR). Catalyst loading was identified as a critical factor: at a low Pt loading of 5 μg_Pt cm⁻², CO adsorption caused a severe decrease in the HOR limiting current, whereas at a high loading of 40 μg_Pt cm⁻², excess available Pt sites markedly diluted the apparent poisoning effect. Electrochemical impedance analysis further revealed distinct loading-dependent changes in interfacial charge-transfer and mass-transport processes under CO-containing conditions. In addition, prolonged CO pre-exposure increased surface poisoning and current loss, while slower LSV scanning amplified the apparent poisoning response by extending the effective CO exposure time. Based on these findings, we propose a standardized RDE evaluation principle based on low catalyst loading, sufficient CO pre-exposure, and slow potential scanning, providing a more reproducible and comparable benchmark for evaluating catalyst-dependent resistance to CO poisoning under controlled RDE conditions. Full article